Implementation of Decision Tree Algorithm to Classify Knowledge Quality in a Knowledge Intensive System

Kaun, Casper; Jhanjhi, N. Z.; Goh, Wei Wei; Sukumaran, S.

doi:10.1051/matecconf/202133504002

Cited by 9 publications

(4 citation statements)

References 44 publications

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“…We screened a number of machine learning regression models before selecting the best one to use as the most appropriate model. The performance of the regression models does not only depend on the quality and quantity of data but are highly influenced by the ML algorithm we are using for the data 21 . In this study, we used data from 71 bead foam extrusion trails.…”

Section: Methodsmentioning

confidence: 99%

“…The performance of the regression models does not only depend on the quality and quantity of data but are highly influenced by the ML algorithm we are using for the data. 21 In this study, we F I G U R E 1 Schematic description of bead foam extrusion mechanism, highlighting the key processes involved (Table 1). [Color figure can be viewed at wileyonlinelibrary.com] used data from 71 bead foam extrusion trails.…”

Section: Modelsmentioning

confidence: 99%

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Machine learning investigation of polylactic acid bead foam extrusion

Shah,

Brütting,

Albuquerque

et al. 2024

J of Applied Polymer Sci

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This study employs machine learning algorithms to analyze the bead foam extrusion process and to assess the impact of processing parameters, specifically focusing on their effects on bead foam density and melt pressure in under water granulation (UWG) for polylactic acid (PLA). These interrelated parameters, influenced by processing parameters such as temperature, screw speed, and blowing agent, possess challenges for traditional empirical methods to capture. The key factors that significantly impact the prediction of melt pressure in UWG are blowing agent, injector pressure, temperature in B‐extruder and die size. Likewise, essential parameters for predicting bead foam density comprise blowing agent, injector pressure, temperature in B‐extruder, die plate temperature, melt temperature in B‐extruder, and melt pressure in B‐extruder. Machine learning (ML) models were employed to forecast bead foam density and melt pressure in UWG using various processing parameters in PLA bead foam extrusion. The random forest model achieved a high coefficient of determination score of 0.96 for predicting melt pressure in UWG. Additionally, the decision tree model demonstrated effective predictions for bead density, with the score: 0.81. These ML models can be applied to diverse materials, leading to more sustainable, efficient processes for bead foam extrusion.

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Section: Methodsmentioning

confidence: 99%

Section: Modelsmentioning

confidence: 99%

Machine learning investigation of polylactic acid bead foam extrusion

Shah,

Brütting,

Albuquerque

et al. 2024

J of Applied Polymer Sci

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“…Consumers' technology readiness positively relates to their perception of technologybased user satisfaction [45]. In particular, users with higher levels of innovation and optimism are more likely to be satisfied [46]. On the other hand, individuals with higher levels of uncertainty and discomfort perceive technology as being more complex, which reduces their chances of high user satisfaction.…”

Section: H9mentioning

confidence: 99%

Organizational Ambidexterity as an Outcome of Quality Dimensions and Triple Helix: The Role of Technology Readiness and User Satisfaction

2022

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Advancing times and rapidly developing technology put pressure and responsibility on the management of organizations. Organizational ambidexterity is a concept for an organization that can balance profitability with innovation and development. This study examined the relationship between the triple helix and quality dimensions on organizational ambidexterity mediated by technology readiness and user satisfaction to give management an advantage in addressing this problem. Quantitative analysis methods using PLS-SEM (Partial Least Square-Structural Equation Modeling) were employed in this study. This study was conducted in Indonesia with 425 respondents participating in the data collection, 411 of which were declared valid after filtering. The results of this study demonstrate that the role of the triple helix in developing organizational ambidexterity is very significant and that other variables, such as quality dimensions and technology readiness, also play an essential role. The framework for organizational ambidexterity presented in this study may be helpful for future research in this field. This study can be further developed for future research, especially by adding new external variables that change over time and focusing more on a specific organization. At the very least, this study is relevant for researchers and practitioners to improve business quality using the concept of the triple helix, quality dimensions, and technology readiness.

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“…CART decision tree is used to calculate the weights of teaching resource information, and the perturbation vector is calculated by combining the decision tree vector of teaching resource information with the mean clustering algorithm. Through constructing the target function of information suitability of teaching resources, we can adjust and reconstruct the category of teaching resource information and achieve the extraction of teaching resource information [23][24][25].…”

Section: College Teaching Information Extractionmentioning

confidence: 99%

Design of Informatization College and University Teaching Management System Based on Improved Decision Tree Algorithm

Liu

Ren

et al. 2022

Wireless Communications and Mobile Computing

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At present, the teaching management system used in colleges cannot classify and store the teaching material information well and also has some problems, such as inaccurate calculation results of resource information weight, long response time, and large data query error. Therefore, this study designs an information college teaching management system based on improved decision tree algorithm. The hardware structure of the system consists of information communication structure, information teaching resource sharing structure, processor, and crystal oscillator circuit, and the core module is the data output control module. This study designs the system software based on the improved decision tree algorithm, creates a decision tree recursively, uses the CART decision tree to calculate the weight of teaching resource information, and constructs the fitness objective function of teaching resource information according to the mean clustering algorithm, so as to accurately extract the teaching resource information and realize the efficient processing of college teaching resource. The experimental results show that the response time of the system in this study is only 8 ms, the maximum convergence value is only 40, there is only one wrong data in the data query, and the storage time is 40 s. This system has a short response time, fast convergent rate, and a low probability of data search error when more than one client access the database at the same time.

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Implementation of Decision Tree Algorithm to Classify Knowledge Quality in a Knowledge Intensive System

Cited by 9 publications

References 44 publications

Machine learning investigation of polylactic acid bead foam extrusion

Machine learning investigation of polylactic acid bead foam extrusion

Organizational Ambidexterity as an Outcome of Quality Dimensions and Triple Helix: The Role of Technology Readiness and User Satisfaction

Design of Informatization College and University Teaching Management System Based on Improved Decision Tree Algorithm

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